{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Finding Lane Lines on the Road** \n",
    "***\n",
    "In this project, you will use the tools you learned about in the lesson to identify lane lines on the road.  You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \"raw-lines-example.mp4\" (also contained in this repository) to see what the output should look like after using the helper functions below. \n",
    "\n",
    "Once you have a result that looks roughly like \"raw-lines-example.mp4\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines.  You can see an example of the result you're going for in the video \"P1_example.mp4\".  Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\n",
    "\n",
    "---\n",
    "Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'.  Run the 2 cells below (hit Shift-Enter or the \"play\" button above) to display the image.\n",
    "\n",
    "**Note** If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from moviepy.editor import VideoFileClip\n",
    "from IPython.display import HTML\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import glob\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This image is: <class 'numpy.ndarray'> with dimesions: (540, 960, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114c5bd30>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAEzCAYAAABOlRseAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDTTQQAD+P58+zFpD5NX7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11083a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reading in an image\n",
    "image = mpimg.imread('test_images/solidWhiteRight.jpg')\n",
    "#printing out some stats and plotting\n",
    "print('This image is:', type(image), 'with dimesions:', image.shape)\n",
    "plt.imshow(image)  #call as plt.imshow(gray, cmap='gray') to show a grayscaled image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def grayscale(img):\n",
    "    \"\"\"Applies the Grayscale transform\n",
    "    This will return an image with only one color channel\n",
    "    but NOTE: to see the returned image as grayscale\n",
    "    you should call plt.imshow(gray, cmap='gray')\"\"\"\n",
    "    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    # Or use BGR2GRAY if you read an image with cv2.imread()\n",
    "    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "def canny(img, low_threshold, high_threshold):\n",
    "    \"\"\"Applies the Canny transform\"\"\"\n",
    "    return cv2.Canny(img, low_threshold, high_threshold)\n",
    "\n",
    "def gaussian_blur(img, kernel_size):\n",
    "    \"\"\"Applies a Gaussian Noise kernel\"\"\"\n",
    "    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n",
    "\n",
    "def region_of_interest(img, vertices):\n",
    "    \"\"\"\n",
    "    Applies an image mask.\n",
    "    \n",
    "    Only keeps the region of the image defined by the polygon\n",
    "    formed from `vertices`. The rest of the image is set to black.\n",
    "    \"\"\"\n",
    "    #defining a blank mask to start with\n",
    "    mask = np.zeros_like(img)   \n",
    "    \n",
    "    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n",
    "    if len(img.shape) > 2:\n",
    "        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "        ignore_mask_color = (255,) * channel_count\n",
    "    else:\n",
    "        ignore_mask_color = 255\n",
    "        \n",
    "    #filling pixels inside the polygon defined by \"vertices\" with the fill color    \n",
    "    cv2.fillPoly(mask, vertices, ignore_mask_color)\n",
    "    \n",
    "    #returning the image only where mask pixels are nonzero\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    return masked_image\n",
    "\n",
    "def draw_lines(img, lines):\n",
    "\n",
    "    # Calculate slopes and sizes of lanes\n",
    "    lines = np.squeeze(lines)\n",
    "    slopes = (lines[:,3]-lines[:,1]) / (lines[:,2] - lines[:,0])\n",
    "    line_size = np.sqrt((lines[:,2] - lines[:,0])**2 + (lines[:,3]-lines[:,1])**2)\n",
    "\n",
    "    # Get rid of outlier lines\n",
    "    slope_threshold = 0.5\n",
    "    lines = lines[np.abs(slopes)> slope_threshold]\n",
    "    line_size = line_size[np.abs(slopes)> slope_threshold]\n",
    "    slopes = slopes[np.abs(slopes)> slope_threshold]\n",
    "\n",
    "    # Seperate positive and negative slopes, lines, and sizes\n",
    "    left_slopes, right_slopes = slopes[slopes>0], slopes[slopes<0]\n",
    "    left_lines, right_lines = lines[slopes>0,:], lines[slopes<0,:]\n",
    "    left_lane_sizes, right_lane_sizes = line_size[slopes>0], line_size[slopes<0]\n",
    "\n",
    "    # lanes sorted by size\n",
    "    left_lane_sizes_sorted = np.argsort(left_lane_sizes)\n",
    "    right_lane_sizes_sorted = np.argsort(right_lane_sizes)\n",
    "\n",
    "    # Calculate average slope on the biggest 6 lines.\n",
    "    left_slope_avg  = left_slopes[left_lane_sizes_sorted][-6::].mean()\n",
    "    right_slope_avg = right_slopes[right_lane_sizes_sorted][-6::].mean()\n",
    "\n",
    "    # find y intercept\n",
    "    # b = y - m * x\n",
    "    left_x_values, left_y_values = np.concatenate([left_lines[:,0], left_lines[:,2]]), np.concatenate([left_lines[:,1], left_lines[:,3]])\n",
    "    right_x_values, right_y_values = np.concatenate([right_lines[:,0],right_lines[:,2]]), np.concatenate([right_lines[:,1], right_lines[:,3]])\n",
    "\n",
    "    left_y_intercept = left_y_values - (left_slope_avg * left_x_values)\n",
    "    right_y_intercept = right_y_values - (right_slope_avg * right_x_values)\n",
    "\n",
    "    # find mean y-intercept based on n biggest lines\n",
    "    left_y_intercept = left_y_intercept[left_lane_sizes_sorted][-6::]\n",
    "    right_y_intercept = right_y_intercept[right_lane_sizes_sorted][-6::]\n",
    "    left_y_intercept_avg, right_y_intercept_avg = np.mean(left_y_intercept), np.mean(right_y_intercept)\n",
    "\n",
    "    imshape = img.shape\n",
    "    img_floor = imshape[0]\n",
    "    horizon_line = imshape[0]/1.5\n",
    "\n",
    "    # calculate new points x = (y - b) / m\n",
    "\n",
    "    # Right lane points\n",
    "    max_right_x = (img_floor - right_y_intercept_avg) / right_slope_avg\n",
    "    min_right_x = (horizon_line - right_y_intercept_avg) / right_slope_avg\n",
    "\n",
    "\n",
    "    # Left lane points\n",
    "    min_left_x = (img_floor - left_y_intercept_avg) / left_slope_avg\n",
    "    max_left_x = (horizon_line - left_y_intercept_avg) / left_slope_avg\n",
    "\n",
    "\n",
    "    l1 = (int(min_left_x), int(img_floor))\n",
    "    l2 = (int(max_left_x), int(horizon_line))\n",
    "    # print('Right points l1 and l2,', l1, l2)\n",
    "    cv2.line(img, l1, l2, [0, 255, 0], 8)\n",
    "\n",
    "    r1 = (int(max_right_x), int(img_floor))\n",
    "    r2 = (int(min_right_x), int(horizon_line))\n",
    "    # print('Left points l1 and l2,', l1, l2)\n",
    "    cv2.line(img, r1, r2, [255, 0, 0], 8)\n",
    "\n",
    "def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n",
    "    \"\"\"\n",
    "    `img` should be the output of a Canny transform.\n",
    "    Returns an image with hough lines drawn.\n",
    "    \"\"\"\n",
    "    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n",
    "    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    \n",
    "    draw_lines(line_img, lines)\n",
    "    return line_img\n",
    "\n",
    "\n",
    "def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):\n",
    "    return cv2.addWeighted(initial_img, α, img, β, λ)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mark_lanes(image):\n",
    "    if image is None: raise ValueError(\"no image given to mark_lanes\")\n",
    "    # grayscale the image to make finding gradients clearer\n",
    "    gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)\n",
    "\n",
    "    # Define a kernel size and apply Gaussian smoothing\n",
    "    kernel_size = 3\n",
    "    blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size), 0)\n",
    "\n",
    "    # Define our parameters for Canny and apply\n",
    "    low_threshold = 50\n",
    "    high_threshold = 150\n",
    "    edges_img = cv2.Canny(np.uint8(blur_gray), low_threshold, high_threshold)\n",
    "\n",
    "\n",
    "    imshape = image.shape\n",
    "    vertices = np.array([[(0, imshape[0]),\n",
    "                          (450, 320),\n",
    "                          (490, 320),\n",
    "                          (imshape[1], imshape[0]) ]],\n",
    "                          dtype=np.int32)\n",
    "\n",
    "    masked_edges = region_of_interest(edges_img, vertices )\n",
    "\n",
    "\n",
    "    # Define the Hough transform parameters\n",
    "    rho             = 2           # distance resolution in pixels of the Hough grid\n",
    "    theta           = np.pi/180   # angular resolution in radians of the Hough grid\n",
    "    threshold       = 5        # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_length = 10       # minimum number of pixels making up a line\n",
    "    max_line_gap    = 20       # maximum gap in pixels between connectable line segments\n",
    "\n",
    "    line_image = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "\n",
    "    # Draw the lines on the image\n",
    "    # initial_img * α + img * β + λ\n",
    "    marked_lanes = cv2.addWeighted(image, 0.8, line_image, 1, 0)\n",
    "    return marked_lanes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test on Images\n",
    "\n",
    "**You might need to create the `marked` directory inside of test_images**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAEkCAYAAABQRik9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRYsWtApBixYtWrRo0QL4/9tZGcnON5oOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233cd588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paths = glob.glob('test_images/*.jpg')\n",
    "\n",
    "for i,image_path in enumerate(paths):\n",
    "    image = mpimg.imread(image_path)\n",
    "    result = mark_lanes(image)\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.imshow(result)\n",
    "    mpimg.imsave('test_images/marked/'+image_path[12:-4]+'_detected.jpg', result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test on Videos\n",
    "\n",
    "You know what's cooler than drawing lanes over images? Drawing lanes over video!\n",
    "\n",
    "We can test our solution on two provided videos:\n",
    "\n",
    "`solidWhiteRight.mp4`\n",
    "\n",
    "`solidYellowLeft.mp4`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the one with the solid white lane on the right first ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video white.mp4\n",
      "[MoviePy] Writing video white.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/222 [00:00<?, ?it/s]\u001b[A\n",
      "  1%|▏         | 3/222 [00:00<00:09, 24.03it/s]\u001b[A\n",
      "  2%|▏         | 5/222 [00:00<00:09, 22.42it/s]\u001b[A\n",
      "  4%|▎         | 8/222 [00:00<00:09, 23.51it/s]\u001b[A\n",
      "  5%|▍         | 11/222 [00:00<00:08, 25.09it/s]\u001b[A\n",
      "  6%|▋         | 14/222 [00:00<00:08, 25.04it/s]\u001b[A\n",
      "  8%|▊         | 17/222 [00:00<00:08, 25.10it/s]\u001b[A\n",
      "  9%|▉         | 20/222 [00:00<00:07, 25.35it/s]\u001b[A\n",
      " 10%|█         | 23/222 [00:00<00:08, 24.44it/s]\u001b[A\n",
      " 12%|█▏        | 26/222 [00:01<00:07, 24.64it/s]\u001b[A\n",
      " 13%|█▎        | 29/222 [00:01<00:07, 25.66it/s]\u001b[A\n",
      " 14%|█▍        | 32/222 [00:01<00:07, 26.38it/s]\u001b[A\n",
      " 16%|█▌        | 35/222 [00:01<00:07, 26.24it/s]\u001b[A\n",
      " 17%|█▋        | 38/222 [00:01<00:09, 19.15it/s]\u001b[A\n",
      " 18%|█▊        | 41/222 [00:01<00:08, 20.60it/s]\u001b[A\n",
      " 20%|█▉        | 44/222 [00:01<00:08, 20.54it/s]\u001b[A\n",
      " 21%|██        | 47/222 [00:02<00:09, 18.11it/s]\u001b[A\n",
      " 22%|██▏       | 49/222 [00:02<00:10, 16.08it/s]\u001b[A\n",
      " 23%|██▎       | 51/222 [00:02<00:10, 16.43it/s]\u001b[A\n",
      " 24%|██▍       | 53/222 [00:02<00:09, 17.16it/s]\u001b[A\n",
      " 25%|██▌       | 56/222 [00:02<00:09, 17.54it/s]\u001b[A\n",
      " 26%|██▌       | 58/222 [00:02<00:09, 18.08it/s]\u001b[A\n",
      " 27%|██▋       | 61/222 [00:02<00:08, 18.92it/s]\u001b[A\n",
      " 29%|██▉       | 64/222 [00:03<00:08, 19.62it/s]\u001b[A\n",
      " 30%|███       | 67/222 [00:03<00:07, 20.23it/s]\u001b[A\n",
      " 32%|███▏      | 70/222 [00:03<00:08, 18.95it/s]\u001b[A\n",
      " 33%|███▎      | 73/222 [00:03<00:07, 19.93it/s]\u001b[A\n",
      " 34%|███▍      | 76/222 [00:03<00:07, 19.99it/s]\u001b[A\n",
      " 36%|███▌      | 79/222 [00:03<00:07, 20.23it/s]\u001b[A\n",
      " 37%|███▋      | 82/222 [00:04<00:07, 17.54it/s]\u001b[A\n",
      " 38%|███▊      | 85/222 [00:04<00:07, 19.18it/s]\u001b[A\n",
      " 40%|███▉      | 88/222 [00:04<00:07, 19.07it/s]\u001b[A\n",
      " 41%|████      | 91/222 [00:04<00:06, 20.15it/s]\u001b[A\n",
      " 42%|████▏     | 94/222 [00:04<00:08, 15.02it/s]\u001b[A\n",
      " 43%|████▎     | 96/222 [00:04<00:08, 14.89it/s]\u001b[A\n",
      " 44%|████▍     | 98/222 [00:05<00:09, 13.46it/s]\u001b[A\n",
      " 45%|████▌     | 100/222 [00:05<00:09, 12.31it/s]\u001b[A\n",
      " 46%|████▌     | 102/222 [00:05<00:10, 11.06it/s]\u001b[A\n",
      " 47%|████▋     | 104/222 [00:05<00:10, 11.22it/s]\u001b[A\n",
      " 48%|████▊     | 106/222 [00:05<00:09, 11.98it/s]\u001b[A\n",
      " 49%|████▊     | 108/222 [00:05<00:09, 12.12it/s]\u001b[A\n",
      " 50%|████▉     | 110/222 [00:06<00:09, 12.34it/s]\u001b[A\n",
      " 50%|█████     | 112/222 [00:06<00:09, 12.14it/s]\u001b[A\n",
      " 51%|█████▏    | 114/222 [00:06<00:11,  9.35it/s]\u001b[A\n",
      " 52%|█████▏    | 116/222 [00:07<00:15,  6.89it/s]\u001b[A\n",
      " 53%|█████▎    | 117/222 [00:07<00:13,  7.59it/s]\u001b[A\n",
      " 54%|█████▎    | 119/222 [00:07<00:11,  9.08it/s]\u001b[A\n",
      " 55%|█████▍    | 121/222 [00:07<00:14,  7.07it/s]\u001b[A\n",
      " 55%|█████▍    | 122/222 [00:07<00:13,  7.20it/s]\u001b[A\n",
      " 55%|█████▌    | 123/222 [00:07<00:13,  7.40it/s]\u001b[A\n",
      " 56%|█████▌    | 124/222 [00:08<00:15,  6.35it/s]\u001b[A\n",
      " 56%|█████▋    | 125/222 [00:08<00:15,  6.43it/s]\u001b[A\n",
      " 57%|█████▋    | 126/222 [00:08<00:14,  6.84it/s]\u001b[A\n",
      " 58%|█████▊    | 128/222 [00:08<00:12,  7.54it/s]\u001b[A\n",
      " 58%|█████▊    | 129/222 [00:08<00:12,  7.40it/s]\u001b[A\n",
      " 59%|█████▊    | 130/222 [00:08<00:11,  7.68it/s]\u001b[A\n",
      " 59%|█████▉    | 131/222 [00:09<00:11,  8.17it/s]\u001b[A\n",
      " 59%|█████▉    | 132/222 [00:09<00:12,  7.23it/s]\u001b[A\n",
      " 60%|█████▉    | 133/222 [00:09<00:12,  7.18it/s]\u001b[A\n",
      " 61%|██████    | 135/222 [00:09<00:10,  8.09it/s]\u001b[A\n",
      " 61%|██████▏   | 136/222 [00:09<00:16,  5.08it/s]\u001b[A\n",
      " 62%|██████▏   | 137/222 [00:10<00:17,  4.81it/s]\u001b[A\n",
      " 62%|██████▏   | 138/222 [00:10<00:14,  5.66it/s]\u001b[A\n",
      " 63%|██████▎   | 140/222 [00:10<00:13,  6.10it/s]\u001b[A\n",
      " 64%|██████▎   | 141/222 [00:10<00:13,  6.14it/s]\u001b[A\n",
      " 64%|██████▍   | 142/222 [00:10<00:12,  6.63it/s]\u001b[A\n",
      " 64%|██████▍   | 143/222 [00:10<00:11,  7.11it/s]\u001b[A\n",
      " 65%|██████▍   | 144/222 [00:11<00:12,  6.41it/s]\u001b[A\n",
      " 66%|██████▌   | 146/222 [00:11<00:10,  7.36it/s]\u001b[A\n",
      " 67%|██████▋   | 148/222 [00:11<00:09,  8.07it/s]\u001b[A\n",
      " 68%|██████▊   | 151/222 [00:11<00:07,  9.80it/s]\u001b[A\n",
      " 69%|██████▉   | 153/222 [00:11<00:07,  9.46it/s]\u001b[A\n",
      " 70%|██████▉   | 155/222 [00:12<00:07,  9.30it/s]\u001b[A\n",
      " 71%|███████   | 157/222 [00:12<00:07,  8.81it/s]\u001b[A\n",
      " 71%|███████   | 158/222 [00:12<00:07,  8.59it/s]\u001b[A\n",
      " 72%|███████▏  | 160/222 [00:12<00:06,  9.59it/s]\u001b[A\n",
      " 73%|███████▎  | 162/222 [00:12<00:05, 11.35it/s]\u001b[A\n",
      " 74%|███████▍  | 165/222 [00:12<00:04, 13.26it/s]\u001b[A\n",
      " 76%|███████▌  | 168/222 [00:12<00:03, 14.91it/s]\u001b[A\n",
      " 77%|███████▋  | 170/222 [00:13<00:03, 16.10it/s]\u001b[A\n",
      " 78%|███████▊  | 173/222 [00:13<00:02, 17.22it/s]\u001b[A\n",
      " 79%|███████▉  | 175/222 [00:13<00:02, 17.20it/s]\u001b[A\n",
      " 80%|███████▉  | 177/222 [00:13<00:02, 16.61it/s]\u001b[A\n",
      " 81%|████████  | 180/222 [00:13<00:02, 17.77it/s]\u001b[A\n",
      " 82%|████████▏ | 183/222 [00:13<00:02, 19.14it/s]\u001b[A\n",
      " 84%|████████▍ | 186/222 [00:13<00:01, 19.71it/s]\u001b[A\n",
      " 85%|████████▌ | 189/222 [00:14<00:02, 14.52it/s]\u001b[A\n",
      " 86%|████████▌ | 191/222 [00:14<00:02, 12.54it/s]\u001b[A\n",
      " 87%|████████▋ | 193/222 [00:14<00:02, 10.94it/s]\u001b[A\n",
      " 88%|████████▊ | 195/222 [00:14<00:02, 10.22it/s]\u001b[A\n",
      " 89%|████████▊ | 197/222 [00:14<00:02, 11.68it/s]\u001b[A\n",
      " 90%|████████▉ | 199/222 [00:15<00:01, 13.26it/s]\u001b[A\n",
      " 91%|█████████ | 202/222 [00:15<00:01, 14.84it/s]\u001b[A\n",
      " 92%|█████████▏| 205/222 [00:15<00:01, 16.45it/s]\u001b[A\n",
      " 94%|█████████▎| 208/222 [00:15<00:00, 17.51it/s]\u001b[A\n",
      " 95%|█████████▍| 210/222 [00:15<00:00, 16.42it/s]\u001b[A\n",
      " 95%|█████████▌| 212/222 [00:15<00:00, 16.15it/s]\u001b[A\n",
      " 96%|█████████▋| 214/222 [00:15<00:00, 16.98it/s]\u001b[A\n",
      " 97%|█████████▋| 216/222 [00:16<00:00, 17.49it/s]\u001b[A\n",
      " 98%|█████████▊| 218/222 [00:16<00:00, 17.92it/s]\u001b[A\n",
      " 99%|█████████▉| 220/222 [00:16<00:00, 18.22it/s]\u001b[A\n",
      "100%|█████████▉| 221/222 [00:16<00:00, 13.57it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: white.mp4 \n",
      "\n",
      "CPU times: user 8.11 s, sys: 1 s, total: 9.11 s\n",
      "Wall time: 17.3 s\n"
     ]
    }
   ],
   "source": [
    "white_output = 'white.mp4'\n",
    "clip1 = VideoFileClip(\"solidWhiteRight.mp4\")\n",
    "white_clip = clip1.fl_image(mark_lanes) #NOTE: this function expects color images!!\n",
    "%time white_clip.write_videofile(white_output, audio=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"white.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**At this point, if you were successful you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)?  Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform.  Modify your draw_lines function accordingly and try re-running your pipeline.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the one with the solid yellow lane on the left. This one's more tricky!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video yellow.mp4\n",
      "[MoviePy] Writing video yellow.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/682 [00:00<?, ?it/s]\u001b[A\n",
      "  0%|          | 3/682 [00:00<00:28, 23.57it/s]\u001b[A\n",
      "  1%|          | 5/682 [00:00<00:42, 15.91it/s]\u001b[A\n",
      "  1%|          | 6/682 [00:00<00:50, 13.30it/s]\u001b[A\n",
      "  1%|          | 8/682 [00:00<00:49, 13.75it/s]\u001b[A\n",
      "  1%|▏         | 10/682 [00:00<00:45, 14.68it/s]\u001b[A\n",
      "  2%|▏         | 12/682 [00:00<00:42, 15.83it/s]\u001b[A\n",
      "  2%|▏         | 15/682 [00:00<00:39, 16.72it/s]\u001b[A\n",
      "  3%|▎         | 18/682 [00:01<00:36, 18.08it/s]\u001b[A\n",
      "  3%|▎         | 21/682 [00:01<00:32, 20.21it/s]\u001b[A\n",
      "  4%|▎         | 24/682 [00:01<00:30, 21.47it/s]\u001b[A\n",
      "  4%|▍         | 27/682 [00:01<00:28, 22.84it/s]\u001b[A\n",
      "  4%|▍         | 30/682 [00:01<00:27, 24.07it/s]\u001b[A\n",
      "  5%|▍         | 34/682 [00:01<00:25, 25.69it/s]\u001b[A\n",
      "  5%|▌         | 37/682 [00:01<00:25, 25.05it/s]\u001b[A\n",
      "  6%|▌         | 40/682 [00:01<00:25, 25.21it/s]\u001b[A\n",
      "  6%|▋         | 43/682 [00:02<00:29, 21.66it/s]\u001b[A\n",
      "  7%|▋         | 46/682 [00:02<00:39, 16.15it/s]\u001b[A\n",
      "  7%|▋         | 48/682 [00:02<01:00, 10.42it/s]\u001b[A\n",
      "  7%|▋         | 50/682 [00:02<00:53, 11.79it/s]\u001b[A\n",
      "  8%|▊         | 53/682 [00:03<00:48, 12.92it/s]\u001b[A\n",
      "  8%|▊         | 55/682 [00:03<00:45, 13.86it/s]\u001b[A\n",
      "  8%|▊         | 57/682 [00:03<00:47, 13.28it/s]\u001b[A\n",
      "  9%|▊         | 59/682 [00:03<00:47, 13.18it/s]\u001b[A\n",
      "  9%|▉         | 61/682 [00:03<01:07,  9.19it/s]\u001b[A\n",
      "  9%|▉         | 63/682 [00:04<01:05,  9.45it/s]\u001b[A\n",
      " 10%|▉         | 65/682 [00:04<01:02,  9.92it/s]\u001b[A\n",
      " 10%|▉         | 67/682 [00:04<00:58, 10.56it/s]\u001b[A\n",
      " 10%|█         | 69/682 [00:04<00:52, 11.58it/s]\u001b[A\n",
      " 10%|█         | 71/682 [00:04<00:54, 11.13it/s]\u001b[A\n",
      " 11%|█         | 73/682 [00:04<00:52, 11.68it/s]\u001b[A\n",
      " 11%|█         | 75/682 [00:05<00:51, 11.80it/s]\u001b[A\n",
      " 11%|█▏        | 77/682 [00:05<00:51, 11.78it/s]\u001b[A\n",
      " 12%|█▏        | 79/682 [00:05<01:01,  9.86it/s]\u001b[A\n",
      " 12%|█▏        | 82/682 [00:05<00:50, 12.00it/s]\u001b[A\n",
      " 12%|█▏        | 84/682 [00:05<00:44, 13.32it/s]\u001b[A\n",
      " 13%|█▎        | 86/682 [00:05<00:42, 13.96it/s]\u001b[A\n",
      " 13%|█▎        | 89/682 [00:05<00:37, 15.81it/s]\u001b[A\n",
      " 13%|█▎        | 91/682 [00:06<00:37, 15.61it/s]\u001b[A\n",
      " 14%|█▎        | 93/682 [00:06<00:45, 12.81it/s]\u001b[A\n",
      " 14%|█▍        | 95/682 [00:06<00:47, 12.33it/s]\u001b[A\n",
      " 14%|█▍        | 98/682 [00:06<00:41, 14.12it/s]\u001b[A\n",
      " 15%|█▍        | 101/682 [00:06<00:36, 15.93it/s]\u001b[A\n",
      " 15%|█▌        | 103/682 [00:06<00:34, 16.58it/s]\u001b[A\n",
      " 15%|█▌        | 105/682 [00:07<00:34, 16.85it/s]\u001b[A\n",
      " 16%|█▌        | 107/682 [00:07<00:39, 14.72it/s]\u001b[A\n",
      " 16%|█▌        | 109/682 [00:07<00:36, 15.72it/s]\u001b[A\n",
      " 16%|█▋        | 112/682 [00:07<00:33, 16.92it/s]\u001b[A\n",
      " 17%|█▋        | 115/682 [00:07<00:31, 18.04it/s]\u001b[A\n",
      " 17%|█▋        | 118/682 [00:07<00:29, 19.20it/s]\u001b[A\n",
      " 18%|█▊        | 121/682 [00:07<00:28, 19.40it/s]\u001b[A\n",
      " 18%|█▊        | 123/682 [00:07<00:28, 19.44it/s]\u001b[A\n",
      " 18%|█▊        | 125/682 [00:08<00:29, 18.99it/s]\u001b[A\n",
      " 19%|█▊        | 127/682 [00:08<00:29, 18.81it/s]\u001b[A\n",
      " 19%|█▉        | 129/682 [00:08<00:29, 18.70it/s]\u001b[A\n",
      " 19%|█▉        | 132/682 [00:08<00:27, 19.77it/s]\u001b[A\n",
      " 20%|█▉        | 135/682 [00:08<00:27, 20.18it/s]\u001b[A\n",
      " 20%|██        | 138/682 [00:08<00:26, 20.75it/s]\u001b[A\n",
      " 21%|██        | 141/682 [00:08<00:26, 20.06it/s]\u001b[A\n",
      " 21%|██        | 144/682 [00:08<00:25, 20.76it/s]\u001b[A\n",
      " 22%|██▏       | 147/682 [00:09<00:25, 20.63it/s]\u001b[A\n",
      " 22%|██▏       | 150/682 [00:09<00:25, 20.77it/s]\u001b[A\n",
      " 22%|██▏       | 153/682 [00:09<00:25, 20.72it/s]\u001b[A\n",
      " 23%|██▎       | 156/682 [00:09<00:25, 20.64it/s]\u001b[A\n",
      " 23%|██▎       | 159/682 [00:09<00:25, 20.58it/s]\u001b[A\n",
      " 24%|██▍       | 162/682 [00:09<00:24, 20.91it/s]\u001b[A\n",
      " 24%|██▍       | 165/682 [00:09<00:24, 21.28it/s]\u001b[A\n",
      " 25%|██▍       | 168/682 [00:10<00:25, 20.43it/s]\u001b[A\n",
      " 25%|██▌       | 171/682 [00:10<00:24, 20.53it/s]\u001b[A\n",
      " 26%|██▌       | 174/682 [00:10<00:24, 20.48it/s]\u001b[A\n",
      " 26%|██▌       | 177/682 [00:10<00:23, 21.06it/s]\u001b[A\n",
      " 26%|██▋       | 180/682 [00:10<00:23, 21.39it/s]\u001b[A\n",
      " 27%|██▋       | 183/682 [00:10<00:23, 21.35it/s]\u001b[A\n",
      " 27%|██▋       | 186/682 [00:11<00:33, 14.65it/s]\u001b[A\n",
      " 28%|██▊       | 188/682 [00:11<00:34, 14.31it/s]\u001b[A\n",
      " 28%|██▊       | 190/682 [00:11<00:43, 11.32it/s]\u001b[A\n",
      " 28%|██▊       | 192/682 [00:11<00:42, 11.42it/s]\u001b[A\n",
      " 28%|██▊       | 194/682 [00:11<00:37, 13.10it/s]\u001b[A\n",
      " 29%|██▉       | 197/682 [00:12<00:32, 14.74it/s]\u001b[A\n",
      " 29%|██▉       | 199/682 [00:12<00:30, 15.99it/s]\u001b[A\n",
      " 30%|██▉       | 202/682 [00:12<00:28, 17.11it/s]\u001b[A\n",
      " 30%|██▉       | 204/682 [00:12<00:28, 16.99it/s]\u001b[A\n",
      " 30%|███       | 206/682 [00:12<00:34, 13.87it/s]\u001b[A\n",
      " 30%|███       | 208/682 [00:12<00:31, 14.99it/s]\u001b[A\n",
      " 31%|███       | 210/682 [00:12<00:29, 16.06it/s]\u001b[A\n",
      " 31%|███       | 212/682 [00:12<00:30, 15.19it/s]\u001b[A\n",
      " 31%|███▏      | 214/682 [00:13<00:33, 14.17it/s]\u001b[A\n",
      " 32%|███▏      | 216/682 [00:13<00:31, 14.93it/s]\u001b[A\n",
      " 32%|███▏      | 218/682 [00:13<00:34, 13.56it/s]\u001b[A\n",
      " 32%|███▏      | 220/682 [00:13<00:33, 13.73it/s]\u001b[A\n",
      " 33%|███▎      | 222/682 [00:13<00:33, 13.76it/s]\u001b[A\n",
      " 33%|███▎      | 224/682 [00:13<00:41, 11.07it/s]\u001b[A\n",
      " 33%|███▎      | 226/682 [00:14<00:37, 12.05it/s]\u001b[A\n",
      " 34%|███▎      | 229/682 [00:14<00:33, 13.64it/s]\u001b[A\n",
      " 34%|███▍      | 231/682 [00:14<00:31, 14.28it/s]\u001b[A\n",
      " 34%|███▍      | 234/682 [00:14<00:28, 15.82it/s]\u001b[A\n",
      " 35%|███▍      | 237/682 [00:14<00:25, 17.42it/s]\u001b[A\n",
      " 35%|███▌      | 239/682 [00:14<00:25, 17.09it/s]\u001b[A\n",
      " 35%|███▌      | 241/682 [00:14<00:28, 15.48it/s]\u001b[A\n",
      " 36%|███▌      | 243/682 [00:15<00:28, 15.68it/s]\u001b[A\n",
      " 36%|███▌      | 245/682 [00:15<00:26, 16.44it/s]\u001b[A\n",
      " 36%|███▌      | 247/682 [00:15<00:30, 14.45it/s]\u001b[A\n",
      " 37%|███▋      | 249/682 [00:15<00:28, 15.30it/s]\u001b[A\n",
      " 37%|███▋      | 251/682 [00:15<00:27, 15.58it/s]\u001b[A\n",
      " 37%|███▋      | 253/682 [00:15<00:38, 11.01it/s]\u001b[A\n",
      " 37%|███▋      | 255/682 [00:16<00:35, 11.90it/s]\u001b[A\n",
      " 38%|███▊      | 257/682 [00:16<00:38, 11.13it/s]\u001b[A\n",
      " 38%|███▊      | 259/682 [00:16<00:34, 12.37it/s]\u001b[A\n",
      " 38%|███▊      | 261/682 [00:16<00:45,  9.15it/s]\u001b[A\n",
      " 39%|███▊      | 263/682 [00:16<00:40, 10.43it/s]\u001b[A\n",
      " 39%|███▉      | 265/682 [00:16<00:34, 12.12it/s]\u001b[A\n",
      " 39%|███▉      | 267/682 [00:17<00:31, 13.34it/s]\u001b[A\n",
      " 40%|███▉      | 270/682 [00:17<00:26, 15.29it/s]\u001b[A\n",
      " 40%|████      | 273/682 [00:17<00:24, 16.56it/s]\u001b[A\n",
      " 40%|████      | 276/682 [00:17<00:27, 14.83it/s]\u001b[A\n",
      " 41%|████      | 278/682 [00:17<00:30, 13.17it/s]\u001b[A\n",
      " 41%|████      | 280/682 [00:17<00:28, 13.93it/s]\u001b[A\n",
      " 41%|████▏     | 282/682 [00:18<00:29, 13.34it/s]\u001b[A\n",
      " 42%|████▏     | 284/682 [00:18<00:26, 14.77it/s]\u001b[A\n",
      " 42%|████▏     | 286/682 [00:18<00:25, 15.55it/s]\u001b[A\n",
      " 42%|████▏     | 289/682 [00:18<00:23, 17.05it/s]\u001b[A\n",
      " 43%|████▎     | 291/682 [00:18<00:23, 16.40it/s]\u001b[A\n",
      " 43%|████▎     | 294/682 [00:18<00:22, 17.21it/s]\u001b[A\n",
      " 43%|████▎     | 296/682 [00:18<00:23, 16.56it/s]\u001b[A\n",
      " 44%|████▎     | 298/682 [00:18<00:22, 17.43it/s]\u001b[A\n",
      " 44%|████▍     | 300/682 [00:19<00:31, 12.32it/s]\u001b[A\n",
      " 44%|████▍     | 302/682 [00:19<00:28, 13.56it/s]\u001b[A\n",
      " 45%|████▍     | 304/682 [00:19<00:26, 14.51it/s]\u001b[A\n",
      " 45%|████▌     | 307/682 [00:19<00:23, 16.03it/s]\u001b[A\n",
      " 45%|████▌     | 310/682 [00:19<00:21, 17.71it/s]\u001b[A\n",
      " 46%|████▌     | 312/682 [00:19<00:20, 18.32it/s]\u001b[A\n",
      " 46%|████▌     | 315/682 [00:19<00:19, 18.74it/s]\u001b[A\n",
      " 46%|████▋     | 317/682 [00:20<00:21, 17.33it/s]\u001b[A\n",
      " 47%|████▋     | 319/682 [00:20<00:20, 17.42it/s]\u001b[A\n",
      " 47%|████▋     | 321/682 [00:20<00:20, 17.49it/s]\u001b[A\n",
      " 47%|████▋     | 323/682 [00:20<00:20, 17.91it/s]\u001b[A\n",
      " 48%|████▊     | 325/682 [00:20<00:20, 17.82it/s]\u001b[A\n",
      " 48%|████▊     | 328/682 [00:20<00:18, 18.82it/s]\u001b[A\n",
      " 48%|████▊     | 330/682 [00:20<00:19, 17.97it/s]\u001b[A\n",
      " 49%|████▊     | 332/682 [00:20<00:19, 18.37it/s]\u001b[A\n",
      " 49%|████▉     | 335/682 [00:21<00:19, 18.23it/s]\u001b[A\n",
      " 49%|████▉     | 337/682 [00:21<00:28, 12.19it/s]\u001b[A\n",
      " 50%|████▉     | 339/682 [00:21<00:32, 10.54it/s]\u001b[A\n",
      " 50%|█████     | 341/682 [00:21<00:28, 11.89it/s]\u001b[A\n",
      " 50%|█████     | 344/682 [00:21<00:27, 12.48it/s]\u001b[A\n",
      " 51%|█████     | 346/682 [00:22<00:30, 10.91it/s]\u001b[A\n",
      " 51%|█████     | 348/682 [00:22<00:40,  8.23it/s]\u001b[A\n",
      " 51%|█████▏    | 350/682 [00:22<00:33,  9.97it/s]\u001b[A\n",
      " 52%|█████▏    | 352/682 [00:22<00:28, 11.50it/s]\u001b[A\n",
      " 52%|█████▏    | 354/682 [00:22<00:25, 13.00it/s]\u001b[A\n",
      " 52%|█████▏    | 356/682 [00:23<00:32,  9.92it/s]\u001b[A\n",
      " 52%|█████▏    | 358/682 [00:23<00:28, 11.35it/s]\u001b[A\n",
      " 53%|█████▎    | 360/682 [00:23<00:24, 12.97it/s]\u001b[A\n",
      " 53%|█████▎    | 362/682 [00:23<00:22, 14.31it/s]\u001b[A\n",
      " 53%|█████▎    | 364/682 [00:23<00:27, 11.43it/s]\u001b[A\n",
      " 54%|█████▍    | 367/682 [00:23<00:23, 13.41it/s]\u001b[A\n",
      " 54%|█████▍    | 369/682 [00:24<00:21, 14.64it/s]\u001b[A\n",
      " 55%|█████▍    | 372/682 [00:24<00:18, 16.35it/s]\u001b[A\n",
      " 55%|█████▍    | 375/682 [00:24<00:17, 17.64it/s]\u001b[A\n",
      " 55%|█████▌    | 377/682 [00:24<00:16, 18.11it/s]\u001b[A\n",
      " 56%|█████▌    | 379/682 [00:24<00:19, 15.86it/s]\u001b[A\n",
      " 56%|█████▌    | 381/682 [00:24<00:18, 15.97it/s]\u001b[A\n",
      " 56%|█████▌    | 383/682 [00:24<00:17, 16.89it/s]\u001b[A\n",
      " 56%|█████▋    | 385/682 [00:24<00:16, 17.59it/s]\u001b[A\n",
      " 57%|█████▋    | 388/682 [00:25<00:15, 18.66it/s]\u001b[A\n",
      " 57%|█████▋    | 391/682 [00:25<00:14, 20.28it/s]\u001b[A\n",
      " 58%|█████▊    | 394/682 [00:25<00:13, 20.99it/s]\u001b[A\n",
      " 58%|█████▊    | 397/682 [00:25<00:13, 21.57it/s]\u001b[A\n",
      " 59%|█████▊    | 400/682 [00:25<00:12, 21.93it/s]\u001b[A\n",
      " 59%|█████▉    | 403/682 [00:25<00:12, 21.98it/s]\u001b[A\n",
      " 60%|█████▉    | 406/682 [00:25<00:12, 22.15it/s]\u001b[A\n",
      " 60%|█████▉    | 409/682 [00:25<00:12, 22.17it/s]\u001b[A\n",
      " 60%|██████    | 412/682 [00:26<00:12, 22.26it/s]\u001b[A\n",
      " 61%|██████    | 415/682 [00:26<00:11, 22.92it/s]\u001b[A\n",
      " 61%|██████▏   | 418/682 [00:26<00:11, 22.96it/s]\u001b[A\n",
      " 62%|██████▏   | 421/682 [00:26<00:11, 22.69it/s]\u001b[A\n",
      " 62%|██████▏   | 424/682 [00:26<00:11, 22.13it/s]\u001b[A\n",
      " 63%|██████▎   | 427/682 [00:26<00:11, 22.09it/s]\u001b[A\n",
      " 63%|██████▎   | 430/682 [00:26<00:11, 22.69it/s]\u001b[A\n",
      " 63%|██████▎   | 433/682 [00:26<00:10, 22.91it/s]\u001b[A\n",
      " 64%|██████▍   | 436/682 [00:27<00:11, 21.97it/s]\u001b[A\n",
      " 64%|██████▍   | 439/682 [00:27<00:11, 20.94it/s]\u001b[A\n",
      " 65%|██████▍   | 442/682 [00:27<00:11, 21.48it/s]\u001b[A\n",
      " 65%|██████▌   | 445/682 [00:27<00:11, 21.51it/s]\u001b[A\n",
      " 66%|██████▌   | 448/682 [00:27<00:10, 21.40it/s]\u001b[A\n",
      " 66%|██████▌   | 451/682 [00:27<00:10, 21.11it/s]\u001b[A\n",
      " 67%|██████▋   | 454/682 [00:27<00:10, 21.78it/s]\u001b[A\n",
      " 67%|██████▋   | 457/682 [00:28<00:10, 21.68it/s]\u001b[A\n",
      " 67%|██████▋   | 460/682 [00:28<00:10, 21.49it/s]\u001b[A\n",
      " 68%|██████▊   | 463/682 [00:28<00:10, 21.31it/s]\u001b[A\n",
      " 68%|██████▊   | 466/682 [00:28<00:09, 21.66it/s]\u001b[A\n",
      " 69%|██████▉   | 469/682 [00:28<00:09, 22.43it/s]\u001b[A\n",
      " 69%|██████▉   | 472/682 [00:28<00:09, 22.29it/s]\u001b[A\n",
      " 70%|██████▉   | 475/682 [00:28<00:09, 22.49it/s]\u001b[A\n",
      " 70%|███████   | 478/682 [00:29<00:08, 22.67it/s]\u001b[A\n",
      " 71%|███████   | 481/682 [00:29<00:08, 23.39it/s]\u001b[A\n",
      " 71%|███████   | 484/682 [00:29<00:08, 23.28it/s]\u001b[A\n",
      " 71%|███████▏  | 487/682 [00:29<00:08, 23.06it/s]\u001b[A\n",
      " 72%|███████▏  | 490/682 [00:29<00:08, 23.50it/s]\u001b[A\n",
      " 72%|███████▏  | 493/682 [00:29<00:08, 23.49it/s]\u001b[A\n",
      " 73%|███████▎  | 496/682 [00:29<00:08, 23.15it/s]\u001b[A\n",
      " 73%|███████▎  | 499/682 [00:29<00:07, 23.42it/s]\u001b[A\n",
      " 74%|███████▎  | 502/682 [00:30<00:07, 22.97it/s]\u001b[A\n",
      " 74%|███████▍  | 505/682 [00:30<00:07, 23.02it/s]\u001b[A\n",
      " 74%|███████▍  | 508/682 [00:30<00:07, 23.10it/s]\u001b[A\n",
      " 75%|███████▍  | 511/682 [00:30<00:07, 22.51it/s]\u001b[A\n",
      " 75%|███████▌  | 514/682 [00:30<00:07, 22.76it/s]\u001b[A\n",
      " 76%|███████▌  | 517/682 [00:30<00:07, 22.31it/s]\u001b[A\n",
      " 76%|███████▌  | 520/682 [00:30<00:07, 21.48it/s]\u001b[A\n",
      " 77%|███████▋  | 523/682 [00:31<00:07, 20.92it/s]\u001b[A\n",
      " 77%|███████▋  | 526/682 [00:31<00:07, 21.19it/s]\u001b[A\n",
      " 78%|███████▊  | 529/682 [00:31<00:07, 20.45it/s]\u001b[A\n",
      " 78%|███████▊  | 532/682 [00:31<00:07, 20.69it/s]\u001b[A\n",
      " 78%|███████▊  | 535/682 [00:31<00:07, 20.09it/s]\u001b[A\n",
      " 79%|███████▉  | 538/682 [00:31<00:07, 19.99it/s]\u001b[A\n",
      " 79%|███████▉  | 541/682 [00:31<00:06, 21.28it/s]\u001b[A\n",
      " 80%|███████▉  | 544/682 [00:32<00:06, 21.05it/s]\u001b[A\n",
      " 80%|████████  | 547/682 [00:32<00:06, 22.26it/s]\u001b[A\n",
      " 81%|████████  | 550/682 [00:32<00:05, 22.69it/s]\u001b[A\n",
      " 81%|████████  | 553/682 [00:32<00:05, 22.78it/s]\u001b[A\n",
      " 82%|████████▏ | 556/682 [00:32<00:05, 22.57it/s]\u001b[A\n",
      " 82%|████████▏ | 559/682 [00:32<00:05, 22.88it/s]\u001b[A\n",
      " 82%|████████▏ | 562/682 [00:32<00:05, 22.44it/s]\u001b[A\n",
      " 83%|████████▎ | 565/682 [00:32<00:05, 22.67it/s]\u001b[A\n",
      " 83%|████████▎ | 568/682 [00:33<00:05, 22.09it/s]\u001b[A\n",
      " 84%|████████▎ | 571/682 [00:33<00:05, 22.14it/s]\u001b[A\n",
      " 84%|████████▍ | 574/682 [00:33<00:04, 22.22it/s]\u001b[A\n",
      " 85%|████████▍ | 577/682 [00:33<00:04, 21.22it/s]\u001b[A\n",
      " 85%|████████▌ | 580/682 [00:33<00:04, 21.57it/s]\u001b[A\n",
      " 85%|████████▌ | 583/682 [00:33<00:04, 21.55it/s]\u001b[A\n",
      " 86%|████████▌ | 586/682 [00:33<00:04, 21.57it/s]\u001b[A\n",
      " 86%|████████▋ | 589/682 [00:34<00:04, 22.04it/s]\u001b[A\n",
      " 87%|████████▋ | 592/682 [00:34<00:03, 22.52it/s]\u001b[A\n",
      " 87%|████████▋ | 595/682 [00:34<00:03, 22.66it/s]\u001b[A\n",
      " 88%|████████▊ | 598/682 [00:34<00:03, 22.27it/s]\u001b[A\n",
      " 88%|████████▊ | 601/682 [00:34<00:04, 19.56it/s]\u001b[A\n",
      " 89%|████████▊ | 604/682 [00:34<00:03, 20.04it/s]\u001b[A\n",
      " 89%|████████▉ | 607/682 [00:34<00:03, 19.60it/s]\u001b[A\n",
      " 89%|████████▉ | 610/682 [00:35<00:03, 20.44it/s]\u001b[A\n",
      " 90%|████████▉ | 613/682 [00:35<00:03, 19.26it/s]\u001b[A\n",
      " 90%|█████████ | 615/682 [00:35<00:03, 18.87it/s]\u001b[A\n",
      " 91%|█████████ | 618/682 [00:35<00:03, 19.56it/s]\u001b[A\n",
      " 91%|█████████ | 620/682 [00:35<00:03, 19.62it/s]\u001b[A\n",
      " 91%|█████████▏| 623/682 [00:35<00:03, 18.69it/s]\u001b[A\n",
      " 92%|█████████▏| 625/682 [00:35<00:03, 18.51it/s]\u001b[A\n",
      " 92%|█████████▏| 627/682 [00:36<00:02, 18.83it/s]\u001b[A\n",
      " 92%|█████████▏| 630/682 [00:36<00:02, 19.53it/s]\u001b[A\n",
      " 93%|█████████▎| 632/682 [00:36<00:02, 18.25it/s]\u001b[A\n",
      " 93%|█████████▎| 634/682 [00:36<00:02, 18.56it/s]\u001b[A\n",
      " 93%|█████████▎| 636/682 [00:36<00:02, 17.77it/s]\u001b[A\n",
      " 94%|█████████▎| 639/682 [00:36<00:02, 18.72it/s]\u001b[A\n",
      " 94%|█████████▍| 642/682 [00:36<00:02, 19.44it/s]\u001b[A\n",
      " 95%|█████████▍| 645/682 [00:36<00:01, 20.58it/s]\u001b[A\n",
      " 95%|█████████▌| 648/682 [00:37<00:01, 20.11it/s]\u001b[A\n",
      " 95%|█████████▌| 651/682 [00:37<00:01, 20.92it/s]\u001b[A\n",
      " 96%|█████████▌| 654/682 [00:37<00:01, 21.18it/s]\u001b[A\n",
      " 96%|█████████▋| 657/682 [00:37<00:01, 20.01it/s]\u001b[A\n",
      " 97%|█████████▋| 660/682 [00:37<00:01, 19.73it/s]\u001b[A\n",
      " 97%|█████████▋| 662/682 [00:37<00:01, 19.65it/s]\u001b[A\n",
      " 98%|█████████▊| 665/682 [00:37<00:00, 20.62it/s]\u001b[A\n",
      " 98%|█████████▊| 668/682 [00:38<00:00, 20.79it/s]\u001b[A\n",
      " 98%|█████████▊| 671/682 [00:38<00:00, 21.10it/s]\u001b[A\n",
      " 99%|█████████▉| 674/682 [00:38<00:00, 21.79it/s]\u001b[A\n",
      " 99%|█████████▉| 677/682 [00:38<00:00, 21.26it/s]\u001b[A\n",
      "100%|█████████▉| 680/682 [00:38<00:00, 21.24it/s]\u001b[A\n",
      "100%|█████████▉| 681/682 [00:38<00:00, 17.61it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: yellow.mp4 \n",
      "\n",
      "CPU times: user 24 s, sys: 2.85 s, total: 26.9 s\n",
      "Wall time: 40.2 s\n"
     ]
    }
   ],
   "source": [
    "yellow_output = 'yellow.mp4'\n",
    "clip2 = VideoFileClip('solidYellowLeft.mp4')\n",
    "yellow_clip = clip2.fl_image(mark_lanes)\n",
    "%time yellow_clip.write_videofile(yellow_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"yellow.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(yellow_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reflections\n",
    "\n",
    "Congratulations on finding the lane lines!  As the final step in this project, we would like you to share your thoughts on your lane finding pipeline... specifically, how could you imagine making your algorithm better / more robust?  Where will your current algorithm be likely to fail?\n",
    "\n",
    "Please add your thoughts below,  and if you're up for making your pipeline more robust, be sure to scroll down and check out the optional challenge video below!\n",
    "\n",
    "\n",
    "## Reflections on Finding Lane Lines\n",
    "\n",
    "\n",
    "This project was such a fantastic (and frustrating) learning experience. \n",
    "\n",
    "I spend a majority of my time at the beginning of the project looking through the forums to get ideas on how other students were approaching the problem as well as understanding how to use numpy and opencv. \n",
    "\n",
    "These are the three approaches I took towards drawing the lanes.\n",
    "\n",
    "\n",
    "\n",
    "### Approach 1 - Extremum\n",
    "\n",
    "The first approach was to simply collect all the points in the lane, find the topmost left and rightmost points and graph lines between them. This worked somewhat well but it lacked the ability to \"fill in\" where there was an absence of lane markings.\n",
    "\n",
    "```python\n",
    "    # Line Extremum Approach\n",
    "    right_lines = np.array(list(filter(lambda x: x[0] > (img.shape[1]/2), lines)))\n",
    "    max_right_x, max_right_y = right_lines.max(axis=0)\n",
    "    min_right_x, min_right_y = right_lines.min(axis=0)\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "### Approach 2 - Average Slope \n",
    "\n",
    "Another, more robust way that should work theorhetically would be to predict the x coordinates based on the average slope and solving for it with the average slope and average y and y intercept.\n",
    "\n",
    "```python\n",
    "    # Average slope Approach\n",
    "    avg_right_slope = right_slopes.mean()\n",
    "\n",
    "    # average point\n",
    "    avg_right_point = np.mean(right_lines, axis=0)\n",
    "\n",
    "    # y intercept of right line\n",
    "    # b = y - mx\n",
    "    right_y_intercept = avg_right_point[1] - avg_right_slope * avg_right_point[0]\n",
    "\n",
    "    # Calculate x values based on average slope and y intercept\n",
    "    max_right_x = int((min_right_y - right_y_intercept)/avg_right_slope)\n",
    "    min_right_x = int((max_right_y - right_y_intercept)/avg_right_slope)\n",
    "    \n",
    "    r1 = (min_right_x, min_right_y)\n",
    "    r2 = (max_right_x, img.shape[0]) # ground to bottom of image\n",
    "```\n",
    "\n",
    "This however would get thrown off by outliers returned by the Hough transform. \n",
    "\n",
    "\n",
    "### Approach 3 - Curve fitting\n",
    "\n",
    "This felt like the most robust way to connect the dots by using numpy to generate a polynomial curve that modeled each lane, and the one that I'm using in my submission. I'm definitely the most happy with the results for this one becuase it allows us to generate sensible x values to complete the lanes when there are dashed lines in the road.\n",
    "\n",
    "```python\n",
    "    # Curve fitting approach\n",
    "    # calculate polynomial fit for the points in right lane\n",
    "    right_curve = np.poly1d(np.polyfit(right_lines[:,1], right_lines[:,0], 2))\n",
    "    left_curve  = np.poly1d(np.polyfit(left_lines[:,1], left_lines[:,0], 2))\n",
    "\n",
    "    # use new curve function f(y) to calculate x values\n",
    "    max_right_x = int(right_curve(img.shape[0]))\n",
    "    min_left_x = int(left_curve(img.shape[0]))\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "### Approach 4 - Return of the ~~Sith~~ 2nd Approach\n",
    "\n",
    "After getting feedback from the instructors on the first submission that used curve fitting as the final approach, I was told to try improving the second approach again, calculating average slope, but I needed to remove outliers. \n",
    "\n",
    "It seems that only considering some of the maximum line lengths and averaging those got us much better results when taking our averages, but the noise can still be seen on a couple of rogue frames. \n",
    "\n",
    "\n",
    "## How could this algorithm be improved? \n",
    "\n",
    "\n",
    "I think we're making an assumption about the \"horizon line\" such that we can use a triangle to generalize about what most roads are going to look like. Curved sections, for example will give us problems since we're only drawing single lines between points. \n",
    "\n",
    "More importantly we currently look for gradients that are rather large in order to find the color changes in the road.\n",
    "This won't be very well suited for dirt roads and lanes who's markings have faded and will have less contrast. Low light conditions will also reduce the contrast in the images we take. \n",
    "\n",
    "There also is something to be said about removing outliers from the points given to us be the hough transformation.\n",
    "For example we could get noise from cars and other lanes (including double lanes or stripes in unique areas).\n",
    "\n",
    "To get rid of the outliers, something like the median gives us a much better sense of where most of the values in a set lie, so we could look in both directions from the median in order to filter out extra points.\n",
    "\n",
    "```\n",
    "def reject_outliers(data, m = 2.):\n",
    "    d = np.abs(data - np.median(data))\n",
    "    mdev = np.median(d)\n",
    "    s = d/mdev if mdev else 0.\n",
    "    return data[s<m]\n",
    "```\n",
    "\n",
    "\n",
    "One last thing thought is that we currently consider the video a set of disjoint frames, resulting in lines that are all drawn independently of each other but we could potentially smooth the lines over sets of frames to prevent the frames from looking too different from each other. \n",
    "\n",
    "If I had more time I would certainly want to look into these options as well as try to trim any `NaN` errors returned from the edge detectoon. (**This is certainly a problem within  the challenge video!**) I appreciated the opportunity to learn and work with Numpy and openCV. It was a fantastic set of tools to work with and I'm really happy to have built a moderately successful result. \n",
    "\n",
    "Thank you to the mentors as well as the office hour instructors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Optional Challenge\n",
    "\n",
    "Try your lane finding pipeline on the video below.  Does it still work?  Can you figure out a way to make it more robust?  If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video extra.mp4\n",
      "[MoviePy] Writing video extra.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/251 [00:00<?, ?it/s]\u001b[A\n",
      "  0%|          | 1/251 [00:00<00:39,  6.32it/s]\u001b[A\n",
      "  1%|          | 2/251 [00:00<00:38,  6.43it/s]\u001b[A"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-94abbddeee19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mclip2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoFileClip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'challenge.mp4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mchallenge_clip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmark_lanes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time challenge_clip.write_videofile(challenge_output, audio=False)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1174\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1177\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-172>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mrequires_duration\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Attribute 'duration' not set\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-171>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36muse_clip_fps_by_default\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m    135\u001b[0m              for (k,v) in k.items()}\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnew_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<decorator-gen-170>\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mconvert_masks_to_RGB\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mismask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_RGB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mwrite_videofile\u001b[0;34m(self, filename, fps, codec, bitrate, audio, audio_fps, preset, audio_nbytes, audio_codec, audio_bitrate, audio_bufsize, temp_audiofile, rewrite_audio, remove_temp, write_logfile, verbose, threads, ffmpeg_params)\u001b[0m\n\u001b[1;32m    337\u001b[0m                            \u001b[0maudiofile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maudiofile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m                            \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthreads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m                            ffmpeg_params=ffmpeg_params)\n\u001b[0m\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mremove_temp\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmake_audio\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/video/io/ffmpeg_writer.py\u001b[0m in \u001b[0;36mffmpeg_write_video\u001b[0;34m(clip, filename, fps, codec, bitrate, preset, withmask, write_logfile, audiofile, verbose, threads, ffmpeg_params)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     for t,frame in clip.iter_frames(progress_bar=True, with_times=True,\n\u001b[0;32m--> 204\u001b[0;31m                                     fps=fps, dtype=\"uint8\"):\n\u001b[0m\u001b[1;32m    205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwithmask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    814\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m    815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 816\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    817\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m                 \u001b[0;31m# Update and print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mgenerator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m                 \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-135>\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(f, *a, **kw)\u001b[0m\n\u001b[1;32m     87\u001b[0m         new_kw = {k: fun(v) if k in varnames else v\n\u001b[1;32m     88\u001b[0m                  for (k,v) in kw.items()}\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0;31m#mf = copy(self.make_frame)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_make_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(gf, t)\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;31m`\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mby\u001b[0m \u001b[0manother\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \"\"\"\n\u001b[0;32m--> 514\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;31m# --------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-32-d954504590a0>\u001b[0m in \u001b[0;36mmark_lanes\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mmax_line_gap\u001b[0m    \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m       \u001b[0;31m# maximum gap in pixels between connectable line segments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     \u001b[0mline_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhough_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasked_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_line_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_line_gap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;31m# Draw the lines on the image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-6eb1ddb0e575>\u001b[0m in \u001b[0;36mhough_lines\u001b[0;34m(img, rho, theta, threshold, min_line_len, max_line_gap)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0mline_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m     \u001b[0mdraw_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mline_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-6eb1ddb0e575>\u001b[0m in \u001b[0;36mdraw_lines\u001b[0;34m(img, lines)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m     \u001b[0mr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_right_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_floor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m     \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_right_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhorizon_line\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;31m# print('Left points l1 and l2,', l1, l2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    }
   ],
   "source": [
    "challenge_output = 'extra.mp4'\n",
    "clip2 = VideoFileClip('challenge.mp4')\n",
    "challenge_clip = clip2.fl_image(mark_lanes)\n",
    "%time challenge_clip.write_videofile(challenge_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"extra.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(challenge_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
